Computational intelligence (CI) combines elements of learning, adaptation, evolution and fuzzy logic that are all closely related to machine learning—to allow one to create, in some sense, intelligent applications. CI techniques typically rely on heuristic algorithms in neural networks, fuzzy systems and evolutionary computation. Each of these algorithms has advantages and disadvantages, and many computer-aided, review and diagnosis (abstractly called CAD) applications have used one of these algorithms (see reference list). However, CAD applications for medical imaging systems often require the integration of several of these algorithms to achieve the efficiency and accuracy needed in the radiology practice.
The present invention overcomes the problems associated with the prior art by optimal and integrated use of multiple machine learning algorithms from the computational intelligence methodologies. The technique in the present invention is specifically applied to medical imaging applications in the domain of computer-aided detection and diagnosis of cancer or other abnormality in the human body using expert knowledge, patient clinical information and images from a variety of modalities, such as, digital mammography, ultrasound, MRI or CT.